Skip to main content
SpringerLink
Log in

Analytical Description of Empirical Probability Distribution Functions

  • Published:
Russian Engineering Research Aims and scope

Abstract

The selection of an analytical expression approximating an empirical probability distribution function is considered. For specific examples, the problems that arise in the analysis of data from simulations and tests of aerospace products are identified. These problems cannot be solved by classical statistical methods. A universal approach based on estimation of the distance between the empirical and hypothetical distribution functions permits the selection of the best solution from those available.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.
Fig. 3.

Similar content being viewed by others

REFERENCES

  1. Hahn, G.J. and Shapiro, S.S., Statistical Models in Engineering, Chichester: Wiley, 1994.

    MATH  Google Scholar 

  2. Kryukov, S.P., Bodrunov, S.D., Aleksandrovskaya, L.N., et al., Metody analiza i otsenivaniya riskov v zadachakh menedzhmenta bezopasnosti slozhnykh sistem (Analysis and Assessment of Risks in the Problems of Safety Management of Complex Technological Systems), St. Petersburg: Aerokosm. Priborostr., 2007.

  3. Bol’shev, L.N. and Smirnov, N.V., Tablitsy matematicheskoi statistiki (Tables of Mathematical Statistics), Moscow: Nauka, 1983.

  4. Aleksandrovskaya, L.N., Aronov, I.Z., Iosifov, P.A., and Kirillin, A.V., Matematicheskie osnovy risk-menedzhmenta tekhnicheskikh sistem. Tom 2. Veroyatnostno-statisticheskie metody otsenki riska. Uchebnoe posobie (Mathematical Principles of Risk Management of Technical Systems, Vol. 2: Probabilistic Statistical Risk Assessment. Manual), Moscow: AIR, 2019.

  5. Cramer, H., Mathematical Methods of Statistics, Uppsala: Almqvist & Wiksells, 1945.

    MATH  Google Scholar 

  6. David, H.A., Order Statistics, New York: Wiley, 1970.

    MATH  Google Scholar 

  7. Aleksandrovskaya, L.N., Mazur, V.N., Khlgatyan, S.V., and Ardalionova, A.E., Forecasting of the unobservable “tails” of distribution functions in the problems of assessment of the compliance of safety requirements for systems of automatic aircraft landing with airworthiness standards, Tr. Mosk. Inst. Elektromekh. Avtom., 2019, no. 5, pp. 38–54.

  8. Lemeshko, B.Yu. and Rogozhnikov, A.P., The features and power of some normality criteria, Metrologiya, 2009, no. 4, pp. 3–24.

Download references

Author information

Authors and Affiliations

  1. Moscow Aviation Institute, Moscow, Russia

    P. A. Iosifov & A. V. Kirillin

Authors
  1. P. A. Iosifov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. V. Kirillin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. V. Kirillin.

Additional information

Translated by B. Gilbert

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Iosifov, P.A., Kirillin, A.V. Analytical Description of Empirical Probability Distribution Functions. Russ. Engin. Res. 40, 669–673 (2020). https://doi.org/10.3103/S1068798X20080122

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S1068798X20080122

Keywords:

Search

Navigation